gradio-dp-machine 0.0.3

Python library for easily interacting with trained machine learning models

gradio_dp_machine

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_dp_machine

Usage

import gradio as gr
from gradio_dp_machine import dp_machine


example = dp_machine().example_value()

demo = gr.Interface(
    lambda x:x,
    dp_machine(),  # interactive version of your component
    dp_machine(),  # static version of your component
    # examples=[[example]],  # uncomment this line to view the "example version" of your component
)


if __name__ == "__main__":
    demo.launch()

dp_machine

Initialization

name	type	default	description
choices	list[str | int | float | tuple[str, str | int | float]] | None	None	A list of string options to choose from. An option can also be a tuple of the form (name, value), where name is the displayed name of the dropdown choice and value is the value to be passed to the function, or returned by the function.
value	str | int | float | Callable | None	None	default value selected in dropdown. If None, no value is selected by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
label	str | None	None	component name in interface.
info	str | None	None	additional component description.
every	float | None	None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
show_label	bool | None	None	if True, will display label.
scale	int | None	None	relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
min_width	int	160	minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
interactive	bool | None	None	if True, choices in this dropdown will be selectable; if False, selection will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
visible	bool	True	If False, component will be hidden.
elem_id	str | None	None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
elem_classes	list[str] | str | None	None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
render	bool	True	If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
key	int | str | None	None	if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.

Events

name	description
change	Triggered when the value of the dp_machine changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See .input() for a listener that is only triggered by user input.
input	This listener is triggered when the user changes the value of the dp_machine.
select	Event listener for when the user selects or deselects the dp_machine. Uses event data gradio.SelectData to carry value referring to the label of the dp_machine, and selected to refer to state of the dp_machine. See EventData documentation on how to use this event data

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As output: Is passed, passes the value of the selected dropdown choice as a str | int | float.

def predict(
    value: str | int | float | None
) -> Unknown:
    return value